A method and system for spatial identification of urban decline

Abstract

This invention discloses a method and system for identifying urban decline spaces, belonging to the field of urban planning technology. The method includes: S1. Acquiring population grid data and building area grid data of a target area for multiple consecutive years within a time interval; S2. Performing spatial alignment and standardization processing on the population grid data and building area grid data to form a standardized annual spatial database; S3. Removing grid units in the annual spatial database that do not possess effective urban activity characteristics to obtain a set of effective urban spatial grids; S4. Performing time series analysis on the effective urban spatial grid set to determine urban decline space grids; S5. Using a spatial clustering algorithm to cluster the determined urban decline space grids, dividing the urban decline space into continuous urban decline space and scattered urban decline space. This invention achieves automatic identification and classification of continuous and scattered urban decline spaces by analyzing the population and building area change trends over multiple consecutive years.

Description

Technical Field

[0001] This invention relates to the field of urban planning technology, and in particular to a method and system for identifying urban decay spaces. Background Technology

[0002] As urbanization deepens, some areas are experiencing continuous population loss, stagnant or even shrinking construction activity, forming "urban decay spaces." Failure to identify these decaying areas promptly and accurately and implement targeted planning measures can easily lead to a vicious cycle of exacerbated population outflow, wasted spatial resources, and declining social service functions. Therefore, scientifically and accurately identifying urban decay spaces has become a crucial prerequisite and foundation for implementing urban renewal, spatial optimization, and sustainable development planning.

[0003] Currently, the identification of urban decay spaces, both domestically and internationally, mainly relies on single-temporal or short-term-series data such as nighttime light data and remote sensing imagery. Common methods include cross-sectional observation and before-and-after comparative analysis. These methods have the following significant drawbacks:

[0004] 1. Lack of time dimension: Most data are based on static data at a certain point in time or a simple comparison between two points in time, which cannot capture the dynamic process and continuous trend of urban spatial decline, and it is difficult to distinguish between short-term fluctuations and long-term decline.

[0005] 2. Single data source: It relies on a single data point (such as nighttime light intensity) and fails to comprehensively reflect the synergistic relationship between population activity and changes in building structures. It is prone to misjudgment due to data noise or local anomalies.

[0006] 3. Simple identification logic: It lacks an identification logic that combines time-series population changes with changes in building activity, lacks a multi-indicator coordination and time-series coherent judgment mechanism, and cannot effectively identify the typical decline pattern of "population decrease and no increase in buildings".

[0007] 4. Lack of spatial type differentiation: The identified decaying spaces were not clustered or classified into different types, making it impossible to distinguish between continuous and scattered decay, which is not conducive to the formulation of differentiated planning policies.

[0008] Therefore, there is an urgent need for a spatial identification method for urban decline that comprehensively considers changes in population and building activities over time. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of existing technologies, such as poor identification accuracy, lack of time dimension, and insufficient spatial clustering. It provides a method and system for identifying urban decay spaces. By analyzing the population and building area change trends over several consecutive years, the decay status of urban spatial units is determined. Combined with spatial autocorrelation analysis and an eight-corner clustering model, the automatic identification and classification of continuous and scattered urban decay spaces are achieved.

[0010] The objective of this invention is achieved through the following technical solution:

[0011] Firstly, a method for identifying urban decay spatial patterns is provided, including the following steps:

[0012] S1. Obtain population grid data and building area grid data for the target area over multiple consecutive years within a time interval;

[0013] S2. Perform spatial alignment and standardization processing on the population grid data and building area grid data to form a standardized annual spatial database;

[0014] S3. Grid cells in the annual spatial database that do not have effective urban activity characteristics are removed to obtain an effective urban spatial grid set;

[0015] S4. Perform time series analysis on the effective urban spatial grid set to determine the urban decay spatial grid;

[0016] S5. A spatial clustering algorithm is used to cluster the identified urban decay spatial grid, dividing the urban decay space into continuous urban decay space and scattered urban decay space.

[0017] In some embodiments, the criteria for determining the urban degradation spatial grid are:

[0018] If the population decreases for several consecutive years and the building area does not increase, then the grid unit is identified as an urban decline spatial grid.

[0019] In some embodiments, the determination criteria are specifically as follows:

[0020] ,in, Representing the spatial grid of urban decay, Represents an effective urban spatial grid set. Indicates the starting year. Indicates the end year The year before, This represents the population value of the i-th grid cell in year t; This represents the building area value of the i-th grid cell in year t; This indicates iterating through all years within a time interval except the last year.

[0021] In some embodiments, a tolerance threshold is set in the determination criteria, and the determination criteria are:

[0022] ,in, Indicates the population tolerance threshold. This indicates the tolerance threshold for building area.

[0023] In some embodiments, step S5 specifically includes:

[0024] Based on the eight-corner relationship, the urban decay spatial grid is divided into several connected regions according to connectivity, and the number of grids in each connected region is counted.

[0025] When the number of grid cells in a connected region exceeds the quantity tolerance threshold T, the region is determined to be a continuous urban decay space; otherwise, it is a scattered urban decay space.

[0026] In some embodiments, the quantity tolerance threshold T is set according to the natural breakpoint method, the average value plus standard deviation of the number of connected region grids, the target region scale, or the grid resolution.

[0027] In some embodiments, the spatial alignment includes:

[0028] Register the grid data for each year using the same coordinate system and correct grid boundary deviations.

[0029] In some embodiments, in step S3, grid cells with a population value of 0 and a building area of ​​0 for several consecutive years are considered grid cells without effective urban activity characteristics; and effective urban areas are automatically identified using a deep learning model based on multi-source spatial data.

[0030] In some embodiments, the steps further include:

[0031] S6. Visualize the results generated in steps S1-S5, including a decay grid spatial distribution map, a connected region statistical map, a decay type distribution map, and a time series variation map.

[0032] Secondly, a spatial identification system for urban decay is provided, including:

[0033] The data acquisition module is used to acquire population grid data and building area grid data for a target area over multiple consecutive years within a time interval.

[0034] The data processing module is used to perform spatial alignment and standardization processing on the population grid data and building area grid data to form a standardized annual spatial database.

[0035] The effective urban space filtering module is used to remove grid cells in the annual spatial database that do not have effective urban activity characteristics, thereby obtaining an effective urban space grid set;

[0036] The urban decay spatial identification module is used to perform time series analysis on the effective urban spatial grid set to determine the urban decay spatial grid.

[0037] The urban decay spatial classification module is used to cluster the identified urban decay spatial grids using spatial clustering algorithms, dividing the urban decay space into continuous urban decay space and scattered urban decay space.

[0038] It should be further noted that the technical features corresponding to the above-mentioned options and embodiments can be combined or substituted with each other to form new technical solutions without conflict.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] 1. This invention proposes a spatial identification mechanism for urban decline that integrates temporal and spatial dimensions. By constructing a time-series database of population and building area, it extracts and standardizes the trends to form a dynamic and objective logic for identifying urban decline. Specifically, this invention integrates two key urban activity indicators—population and building area—to overcome the limitations of a single data source and enhance the reliability and comprehensiveness of the identification results. Through time-series analysis of population and building data over several consecutive years, it can capture the dynamic process of urban decline and distinguish between long-term trends and short-term fluctuations. This improved approach enables dynamic detection and quantitative analysis of the urban spatial decline process, improving the accuracy and timeliness of the identification results.

[0041] 2. This invention introduces a clustering algorithm to construct an automatic clustering mechanism based on spatial domain relationships, enabling adaptive identification and classification of decaying regions. This mechanism effectively identifies the spatial clustering and diffusion characteristics of urban decaying spaces, reducing biases caused by human intervention and thus improving the objectivity and reliability of the identification. Specifically, an eight-corner clustering algorithm is introduced to achieve adaptive clustering and type classification of decaying spaces, distinguishing between continuous and scattered decay, providing a basis for differentiated planning.

[0042] 3. This invention supports multi-scale grid analysis, can be adapted to different research scopes such as urban areas and city areas, and can integrate multi-source auxiliary data such as land use and remote sensing to enhance the applicability and flexibility of the method. Attached Figure Description

[0043] Figure 1 This is a flowchart of a method for spatial identification of urban decay according to the present invention;

[0044] Figure 2 This is a partial spatial diagram of urban decay provided by the present invention, used to illustrate the distribution of the identified decay grid.

[0045] Figure 3 A schematic diagram illustrating the spatial type classification logic of urban decay based on eight-neighbor clustering provided by the present invention;

[0046] Figure 4A schematic diagram illustrating the number and statistics of grids in the spatially connected areas of urban degradation, provided by this invention.

[0047] Figure 5 This is a partial visualization example of urban decay spatial types provided by the present invention, used to illustrate the distribution characteristics of continuous decay and scattered decay. Detailed Implementation

[0048] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] It should be noted that the defects in the solutions in the prior art are all the results of the inventors' practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed by the embodiments of this application in the following text should be the inventors' contributions to this application in the process of invention and creation, and should not be understood as technical content known to those skilled in the art.

[0050] In view of the technical problems pointed out in the background art, the present invention provides the following embodiments:

[0051] In one exemplary embodiment, a method for identifying urban decay space is provided, such as... Figure 1 As shown, it includes the following steps:

[0052] S1. Obtain the target region within the time interval Population grid data for consecutive years and building area grid data ;in The starting year, For the end year, ;

[0053] S2. Perform spatial alignment and standardization processing on the population grid data and building area grid data to form a standardized annual spatial database. ;

[0054] S3. Remove grid cells from the annual spatial database that do not possess effective urban activity characteristics to obtain the effective urban spatial grid set. ;

[0055] S4. Perform time series analysis on the effective urban spatial grid set to determine the urban decay spatial grid. ;

[0056] S5. A spatial clustering algorithm is used to cluster the identified urban decay spatial grid, dividing the urban decay space into continuous urban decay space and scattered urban decay space.

[0057] Specifically, the grid scale can be selected based on the size and depth of the target area. For example, a 500m×500m grid can be used for macroscopic areas, while a 100m×100m high-precision grid can be used for urban central areas to balance computational efficiency and spatial resolution.

[0058] In one implementation, population grid data and building area grid data can be derived from raster data published by statistical departments, or calculated from census data, mobile signaling data, building outline data, etc. Both types of data need to cover the same spatial area and have temporal continuity.

[0059] In one implementation, interpolation can be used to estimate locally missing data.

[0060] In one implementation, multi-year training samples are constructed using other external data sources (such as land use data, remote sensing feature data, etc.), and missing values ​​are imputed using a machine learning model to ensure the integrity and continuity of the data.

[0061] In one implementation, the spatial alignment includes:

[0062] Register the grid data for each year using the same coordinate system (such as WGS84) and correct grid boundary deviations.

[0063] In one implementation, in step S3, grid cells with a population value of 0 and a building area of ​​0 for several consecutive years are considered grid cells without effective urban activity characteristics; and based on multi-source spatial data such as urban built-up land data and urban land use data, a deep learning model is used to automatically identify effective urban areas, thereby obtaining an effective urban spatial grid set.

[0064] In one implementation, the criteria for determining the urban decay spatial grid are:

[0065] If the population decreases for several consecutive years and the building area does not increase, then the grid unit is identified as an urban decline spatial grid.

[0066] The specific criteria for determining the urban decay spatial grid are as follows:

[0067]

[0068] in, Representing the spatial grid of urban decay, Represents an effective urban spatial grid set. Indicates the starting year. Indicates the end year The year before, This represents the population value of the i-th grid cell in year t; This represents the building area value of the i-th grid cell in year t; ∀t∈ This indicates iterating through all years within the time interval except the last year. In practice, this condition can be achieved by iterating through a time series. If for any... All satisfy and If so, the grid cell is labeled as an urban decay spatial grid.

[0069] In practical implementation, to avoid accidental fluctuations, a tolerance threshold is set in the judgment condition, and the judgment condition is rewritten as follows:

[0070] ,in, Indicates the population tolerance threshold. This indicates the tolerance threshold for building area.

[0071] In one implementation, the spatial clustering algorithm uses an eight-corner relationship (i.e., adjacent units in the vertical, horizontal, and four diagonal directions) for spatial connectivity analysis. The urban decay spatial grid is divided into several connected regions based on connectivity. , ,…… The number of grid cells within each connected region is counted and denoted as . A quantity tolerance threshold T is set to distinguish between continuous and scattered urban decay spaces. When the number of grid cells in the connected region... If the value is greater than T, the area is determined to be a continuous urban recession space; otherwise, it is a scattered urban recession space.

[0072] The quantity tolerance threshold T is set according to the natural breakpoint method, the average value plus standard deviation method of the number of grids in the connected region, the target region scale, or the grid resolution.

[0073] In some embodiments, the method of the present invention further includes the step of:

[0074] S6. Visualize the results generated in steps S1-S5. These results include a spatial distribution map of the decay grid, a statistical map of connected regions, a distribution map of decay types, and a time-series variation map. Visualizing the results through a GIS system provides support for urban planning decisions.

[0075] The method of this invention combines population and building time-series grid data to achieve automatic identification and classification of urban decaying spaces, which can provide data support for urban renewal, spatial governance and macro policy formulation.

[0076] The following uses statistical data from a typical city between 2014 and 2024 as an example to illustrate the specific implementation process of the method of the present invention.

[0077] Get target city Population grid data for consecutive years within the range [2014, 2024]. and building area grid data , where i represents the grid cell number and t represents the year.

[0078] In this embodiment, a standard grid of 250m×250m is used as the spatial analysis unit. This grid scale can reflect the urban spatial pattern and control the amount of data processing, making it suitable for urban decay spatial identification within the city area.

[0079] Population grid data can be derived from annual rasterized data, census data, or mobile signaling data released by statistical departments; building area grid data can be derived from annual rasterized data released by statistical departments or calculated using land use data or appropriate building outline data. Both types of data need to cover the same spatial area and have temporal continuity.

[0080] It should be noted that in specific implementation methods, the use, collection, and processing of relevant data must strictly comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0081] In this embodiment, the WGS84 coordinate system is used to spatially register the population and building area grid data for each year.

[0082] Furthermore, the data from different years are standardized, including unit unification and outlier handling, to form a standardized annual spatial database. .

[0083] Based on the steps described above, comparability between different years and different indicators can be ensured.

[0084] After obtaining the annual spatial database, grid cells with a population of 0 and a building area of ​​0 during the observation period of 2014-2024 were removed. These grid cells typically correspond to undeveloped areas or natural ecological zones and are not of practical significance for identifying urban decay spaces.

[0085] The set of grids that have been removed is denoted as the effective urban spatial grid set. This set contains only effective urban spatial units with population activity and building distribution.

[0086] In this embodiment, time series analysis is performed on each valid urban grid unit to identify its population and building area trends between 2014 and 2024.

[0087] First, for each grid cell Extract its continuous population sequence within the time interval [2014, 2024]:

[0088]

[0089] And the building area sequence:

[0090]

[0091] Secondly, the time series differencing and trend judgment method is used to compare adjacent time series data for each year. If a grid cell shows "population decreasing year by year" and "building area not increasing" within the time interval [2014, 2024], then the grid cell is identified as an urban decline spatial grid.

[0092] In this embodiment, the formal definition of the urban decay spatial grid is as follows:

[0093]

[0094] To enhance the robustness and practicality of the model, this embodiment can further set dynamic threshold conditions. That is, when the population decline exceeds a set threshold and the building area growth rate is lower than a set threshold, the grid cell will also be identified as a declining grid.

[0095] Based on the above methods, areas experiencing continuous population loss over time and stagnation of construction activity in space can be comprehensively identified, enabling dynamic and objective identification of urban decay spaces. For example... Figure 2 The image shown is a partial schematic diagram of the urban decay space obtained in this embodiment.

[0096] In this embodiment, the identified urban spatial degradation grid set is... The eight-neighbor clustering algorithm is used for spatial clustering and type classification.

[0097] The term "octagonal relationship" refers to the spatial connectivity between each grid cell and its adjacent grid cells in the top, bottom, left, right, and four diagonal directions. If two grid cells are in contact in the octagonal directions, they are considered to belong to the same connected region. Figure 3 This is a schematic diagram illustrating the spatial classification logic of urban decay based on eight-neighbor clustering, provided in an embodiment of this application.

[0098] Using the adjacency rules described above, all decaying grid cells can be divided into several spatially connected regions, denoted as . , ,…… , where m is the total number of all connected regions.

[0099] For each connected region Calculate the number of decay grids it contains. .

[0100] Furthermore, by setting a tolerance threshold T for the number of connected regions, it is possible to distinguish between continuous urban decay space and scattered urban decay space.

[0101] In this embodiment, the tolerance threshold is set to a natural breakpoint value of 14. When the number of grids in a connected region is greater than 14, the region is determined to be a continuous urban decay space; otherwise, it is a scattered urban decay space.

[0102] In one possible implementation, the threshold can be set using the average number of grid cells in each connected region plus its standard deviation. It can also be flexibly set according to the scale of the study area or the grid resolution.

[0103] The above methods can be used to classify urban decay spaces into different types. Figure 4 This is a schematic diagram showing the number and statistics of grids in the spatial connectivity area of ​​urban decline provided in the embodiments of this application.

[0104] In this embodiment, by using ArcGIS Pro to map the urban decay spatial grid and its type to the urban spatial coordinate system, a visualization of the spatial distribution of urban decay is generated. For example... Figure 5 The image shown is a partial visualization example of urban decay spatial types provided in this application embodiment, used to illustrate the distribution characteristics of continuous decay and scattered decay.

[0105] In another exemplary embodiment, an urban decay spatial identification system is provided, comprising:

[0106] The data acquisition module is used to acquire population grid data and building area grid data for a target area over multiple consecutive years within a time interval.

[0107] The data processing module is used to perform spatial alignment and standardization processing on the population grid data and building area grid data to form a standardized annual spatial database.

[0108] The effective urban space filtering module is used to remove grid cells in the annual spatial database that do not have effective urban activity characteristics, thereby obtaining an effective urban space grid set;

[0109] The urban decay spatial identification module is used to perform time series analysis on the effective urban spatial grid set to determine the urban decay spatial grid.

[0110] The urban decay spatial classification module is used to cluster the identified urban decay spatial grids using spatial clustering algorithms, dividing the urban decay space into continuous urban decay space and scattered urban decay space.

[0111] Specifically, the data acquisition module can further acquire external auxiliary data such as land use data, built-up area data, and remote sensing image data to supplement urban spatial information with multi-source information. It also performs the following operations:

[0112] The system connects to external databases and data sources through a data interface unit; it organizes and labels annual data through a time slice management unit; and it crops the research area through a region clipping unit, thereby obtaining a multi-source dataset that meets research needs.

[0113] The data processing module performs the following operations:

[0114] Used to spatially register and align population grid data with building area grid data, achieving spatial uniformity between different data sources;

[0115] It is also used for cleaning, anomaly detection, gridding, and time-series alignment of multi-source data to ensure data quality and consistency;

[0116] It is also used to complete missing data by using external auxiliary data and machine learning models to form a complete dataset;

[0117] It is also used to merge different data sources to generate urban grid datasets with a unified structure and consistent time sequence.

[0118] The data cleaning and detection unit performs outlier detection and noise filtering on the original population grid data, building area grid data, and auxiliary data.

[0119] The missing data is filled in by a data completion and time-series alignment unit, while the data from different years are aligned in time series. This unit can complete local missing values ​​based on interpolation, or it can use external auxiliary data combined with machine learning models to predict missing values, thereby forming complete and continuous multi-year grid data.

[0120] The effective urban space filtering module performs the following operations:

[0121] By using cleaned and completed data, effective urban spatial extraction units are used to identify urban activity grids. Specifically, this includes screening grids that have been uninhabited for a long time and have no building activity, combining auxiliary data to determine urban boundaries and functional areas, and outputting an effective urban spatial grid set to provide a foundation for subsequent urban decay identification.

[0122] The urban decline space identification module determines whether a grid belongs to an urban decline space based on multi-year population changes, building area changes, and other indicators. Specifically, a threshold determination unit calculates multi-year population changes, building area changes, or other relevant indicators for each grid and determines whether a grid belongs to an urban decline space based on preset thresholds or rules.

[0123] The urban decay spatial classification module uses the spatial type determination unit to determine the type of urban decay space a decay area belongs to based on cluster analysis results, the scale, connectivity, and morphological characteristics of the decay area.

[0124] Furthermore, the system also includes a visualization and result output module, which is used to generate urban decay spatial grid distribution maps, connected area statistical maps or tables, decay type distribution maps and time series change maps, etc., and can also output the results to external systems through interfaces for further analysis or decision support.

[0125] The above detailed embodiments are a description of the present invention. It should not be considered that the specific embodiments of the present invention are limited to these descriptions. For those skilled in the art, several simple deductions and substitutions can be made without departing from the concept of the present invention, and all of these should be considered to fall within the protection scope of the present invention.

Claims

1. A method for spatial identification of urban decay, characterized in that, Includes the following steps: S1. Obtain population grid data and building area grid data for the target area over multiple consecutive years within a time interval; S2. Perform spatial alignment and standardization processing on the population grid data and building area grid data to form a standardized annual spatial database; S3. Grid cells in the annual spatial database that do not have effective urban activity characteristics are removed to obtain an effective urban spatial grid set; S4. Perform time series analysis on the effective urban spatial grid set to determine the urban decay spatial grid; the determination criteria for the urban decay spatial grid are as follows: If the population decreases for several consecutive years and the building area does not increase, then the grid unit is identified as an urban decline space grid. The specific judgment criteria are as follows: ,in, Representing the spatial grid of urban decay, Represents an effective urban spatial grid set. Indicates the starting year. Indicates the end year The year before, This represents the population value of the i-th grid cell in year t; This represents the building area value of the i-th grid cell in year t; This indicates iterating through all years within a time interval, excluding the last year. A tolerance threshold is set in the determination criteria, and the determination criteria are as follows: ,in, Indicates the population tolerance threshold. Indicates the tolerance threshold for building area; S5. A spatial clustering algorithm is used to cluster the identified urban decay spatial grid, dividing the urban decay space into continuous urban decay space and scattered urban decay space; step S5 specifically includes: Based on the eight-corner relationship, the urban decay spatial grid is divided into several connected regions according to connectivity, and the number of grids in each connected region is counted. When the number of grid cells in a connected region exceeds the quantity tolerance threshold T, the region is determined to be a continuous urban decay space; otherwise, it is a scattered urban decay space.

2. The method for spatial identification of urban decay according to claim 1, characterized in that, The quantity tolerance threshold T is set based on the natural breakpoint method, the average value plus standard deviation method of the number of grids in the connected region, the target region scale, or the grid resolution.

3. The method for spatial identification of urban decline according to claim 1, characterized in that, The spatial alignment includes: Register the grid data for each year using the same coordinate system and correct grid boundary deviations.

4. The method for spatial identification of urban decline according to claim 1, characterized in that, In step S3, grid cells with a population value of 0 and a building area of ​​0 for several consecutive years are considered to be grid cells without effective urban activity characteristics; and based on multi-source spatial data, a deep learning model is used to automatically identify effective urban areas.

5. The method for spatial identification of urban decline according to claim 1, characterized in that, It also includes the following steps: S6. Visualize the results generated in steps S1-S5, including a decay grid spatial distribution map, a connected region statistical map, a decay type distribution map, and a time series variation map.

6. A spatial identification system for urban decline, characterized in that, include: The data acquisition module is used to acquire population grid data and building area grid data for a target area over multiple consecutive years within a time interval. The data processing module is used to perform spatial alignment and standardization processing on the population grid data and building area grid data to form a standardized annual spatial database. The effective urban space filtering module is used to remove grid cells in the annual spatial database that do not have effective urban activity characteristics, thereby obtaining an effective urban space grid set; The urban decay spatial identification module is used to perform time series analysis on the effective urban spatial grid set to determine the urban decay spatial grids; the determination criteria for the urban decay spatial grids are as follows: If the population decreases for several consecutive years and the building area does not increase, then the grid unit is identified as an urban decline space grid. The specific judgment criteria are as follows: ,in, Representing the spatial grid of urban decay, Represents an effective urban spatial grid set. Indicates the starting year. Indicates the end year The year before, This represents the population value of the i-th grid cell in year t; This represents the building area value of the i-th grid cell in year t; This indicates iterating through all years within a time interval, excluding the last year. A tolerance threshold is set in the determination criteria, and the determination criteria are as follows: ,in, Indicates the population tolerance threshold. Indicates the tolerance threshold for building area; The urban decay spatial classification module is used to cluster the identified urban decay spatial grids using a spatial clustering algorithm, dividing the urban decay space into continuous urban decay space and scattered urban decay space; the specific steps of clustering the identified urban decay spatial grids using the spatial clustering algorithm include: Based on the eight-corner relationship, the urban decay spatial grid is divided into several connected regions according to connectivity, and the number of grids in each connected region is counted. When the number of grid cells in a connected region exceeds the quantity tolerance threshold T, the region is determined to be a continuous urban decay space; otherwise, it is a scattered urban decay space.

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